The present invention relates generally to diagnosing neurological disorders, and more particularly to a method and apparatus which utilize a trained computational intelligence system to classify movement of a patient based upon neurological disorder classifications.
Several symptoms associated with Parkinson""s disease (PD) have been documented such as lack of facial expression, tremor of the distal segments of the limbs at rest, muscular rigidity, bradykenesia, and postural abnormalities. However, the exact diagnosis of PD in an early phase of the disease is complicated because other neurological disorders such as essential tremor (ET) have similar early symptoms. According to international statistics, the diagnosis of more than 20% of patients in an early phase of a neurological disorder is false. Moreover, the diagnosis for a great number of patients is typically uncertain in the first 2-3 years of the neurological disorder. This 2-3 years of uncertainty results in a more expensive and less effective treatment of the patient when compared with appropriate treatment started in an earlier phase of the disorder.
Therefore a need exists for a method and apparatus that are operable to accurately diagnose neurological disorders in an early phase of the disorder.
Definition of Terms Associated with Computational Intelligence
Computational intelligence as used herein refers to a methodology involving computing (usually with a computer) that exhibits an ability to learn and/or to deal with new situations, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association, and abstraction. Computational intelligence systems usually comprise hybrids of paradigms such as artificial neural networks, fuzzy systems, and evolutionary computation systems, augmented with knowledge elements. Computational intelligence systems are often designed to mimic one or more aspects of biological intelligence.
A paradigm as used herein refers to a particular choice of computational intelligence attributesxe2x80x94in the case of a neural network, the architecture, activation and learning rules, update procedure, and so onxe2x80x94that exhibits a certain type of behavior. In other words, a paradigm is a specific example of a computational intelligence system.
An artificial neural network (ANN) is used herein to refer to an analysis paradigm that is roughly modeled after the massively parallel structure of a biological neural network such as the human brain. An ANN is typically implemented with many relatively simple processing elements (PEs) that are interconnected by many weighted connections in order to obtain a computational structure that simulates the highly interconnected, parallel computational structure of biological neural networks. Hereinafter, the terms network and neural network are used interchangeably with the term artificial neural network.
The term evolutionary computation is used herein to refer to machine learning optimization and classification paradigms that are roughly based on evolutionary mechanisms such as biological genetics and natural selection. The evolutionary computational field includes genetic algorithms, evolutionary programming, genetic programming, evolution strategies, and particle swarm optimization.
Genetic algorithms are search algorithms that incorporate natural evolution mechanisms including crossover, mutation, and survival of the fittest. Genetic algorithm paradigms work on populations of individuals, rather than on single data points or vectors. Genetic algorithms are more often used for optimization, but may also be used for classification.
Evolutionary programming algorithms are similar to genetic algorithms, but do not incorporate crossover. Rather, evolutionary programming algorithms rely on survival of the fittest and mutation.
A particle swarm, as used herein, is similar to a genetic algorithm (GA) in that the system is initialized with a population of randomized positions in hyperspace that represent potential solutions to an optimization problem. However, each particle of a particle swarm, unlike a GA, is also assigned a randomized velocity. The particles (i.e. potential solutions) are then xe2x80x9cflownxe2x80x9d through hyperspace based upon their respective velocities in search of an optimum solution to a global objective.
Fuzzy sets model the properties of imprecision, approximation, or vagueness. In conventional logic an element either is or is not a member of the set. In other words, in conventional logic each element has a membership value of either 1 or 0 in the set. In a fuzzy set, however, each fuzzy membership value reflects the membership extents (or grades) of an element in the set which is often represented with values ranging between and including 0 and 1.
Fuzzy logic is the logic of xe2x80x9capproximate reasoning.xe2x80x9d Fuzzy logic comprises operations on fuzzy sets including equality, containment, complementation, intersection, and union.
A fuzzy expert system as used herein, is a decision support rule-based expert system that uses fuzzy rules and fuzzy sets. A fuzzy expert system is more compact (i.e. has fewer roles) than traditional expert systems, and the rules are more consistent with the way knowledge is expressed in natural language. The system can represent multiple cooperating, and even conflicting experts.
The above terms as well as others associated with computational intelligence systems are defined in further detail in Russell C. Eberhart, et al., Computational Intelligence PC Tools (1996), the disclosure of which is hereby incorporated by reference.
The present invention is directed toward fulfilling the need for a method and apparatus that are operable to accurately diagnose neurological disorders. In accordance with one embodiment of the present invention, there is provided a method of diagnosing patients suspected of having a neurological disorder. One step of the method includes monitoring movement of a patient in order to obtain movement data that is representative of the movement of the patient. Another step of the method includes processing the movement data in order to obtain an input pattern that is representative of the movement data. The method also includes the step of processing the input pattern with a computational intelligence system that has been trained to classify movement based upon a predetermined group of neurological disorder classifications. Furthermore, the method includes generating with the computational intelligence system an output that is indicative of an appropriate neurological disorder classification for the patient.
Pursuant to another embodiment of the present invention, there is provided an analysis system for diagnosing patients suspected of having a neurological disorder. The analysis system includes a movement monitoring device, a preprocessor, and computational intelligence. The movement monitoring device of the analysis system is operable to monitor movement of a patient over a collection period in order to obtain movement data that is representative of the movement of the patient over the collection period. The preprocessor of the analysis system is operable to generate an input pattern that is representative of the movement data collected by the movement monitoring device over the collection period. The computational intelligence system includes a neural network that has been trained to classify movement based upon a predetermined group of neurological disorder classifications. In particular, the neural network is operable to (i) process the input pattern generated by the preprocessor, and (ii) generate an output that is indicative of an appropriate neurological disorder classification for the patient.
Pursuant to yet another embodiment of the present invention, there is provided a computer readable medium that configures an analysis system for diagnosing patients suspected of having a neurological disorder. The computer readable medium includes instructions which when executed by the analysis system cause the analysis system to generate an input pattern from movement data representative of movement of a patient over a collection period. The instructions of the computer readable medium when executed by the analysis system further cause the analysis system to implement a neural network trained to classify the movement of the patient based upon a predetermined group of neurological disorder classifications. Moreover, the instructions when executed by the analysis system cause the analysis system to process the input pattern with the neural network to obtain an appropriate neurological disorder classification for the patient. The instructions of the computer readable medium when executed by the analysis system also cause the analysis system to display output providing an indication of the appropriate neurological disorder classification for the patient.
It is an object of the present invention to provide a new and useful method and apparatus for diagnosing neurological disorders.
It is also an object of the present invention to provide an improved method and apparatus for diagnosing neurological disorders.
It is another object of the present invention to provide a method and apparatus for diagnosing a neurological disorder in an early phase of the neurological disorder.
It is yet another object of the present invention to provide a method and apparatus which utilize trained computational intelligence to classify movement based upon neurological disorder classifications.
The above and other objects, features, and advantages of the present invention will become apparent from the following description and the attached drawings.